Key Points
1. The model discussed in the paper struggles with counting and spatial reasoning despite improvements from fine-tuning, leading to the generation of an extra text field and misplaced buttons in the scene.
2. The paper provides example pairs of code captured with different abstract syntax tree (AST) match rates and an example of code pairs obtained using embedding distance as a measure of similarity.
3. When the AST match rate is 1.0, the coding problems require the same reasoning but have drastically different prompts, implicitly teaching the model the basic reasoning task of finding the closest pair of elements in an array.
4. When the AST match rate is 0.96, the problems use similar reasoning and coding concepts, but their prompts ask for different tasks - returning a pair of numbers versus computing their average.
5. When the AST match rate is ≤ 0.9, the code pairs start to become less similar, as shown in two examples where the AST match rate is 0.9 and 0.83, respectively.
6. The paper discusses the application of embedding distance as a measure of similarity, showing that code pairs with similar Python Docstrings, function names, and code structure can be identified using the L2 distance between the normalized CodeGen-Mono 350M embeddings.
7. A specific example is provided where two problems have similar Python Docstrings, function names, and code structure, with an embedding distance of 0.16.
8. The paper discusses specific coding problems related to finding closest elements, finding closest pair average, increasing values in a list, finding all prefixes of a string, rescaling numbers to a unit, and plotting frequency ranges.
9. Lastly, the paper highlights the importance of utilizing AST match rates and embedding distances to assess code similarity and guide the development of models for coding problems.
Summary
The research paper introduces "phi-1," a new large language model for code, with significantly smaller size than other competing models. The model, a transformer-based one with 1.3B parameters, is trained using a combination of "textbook quality" data from the web and synthetically generated textbooks and exercises using GPT-3.5. Despite its smaller scale, phi-1 achieves high accuracy on HumanEval and MBPP benchmarks and displays surprising emergent properties compared to other models.
The paper challenges the existing scaling laws and demonstrates that high-quality data dramatically changes the shape of the scaling laws, potentially allowing smaller models to match the performance of larger-scale models. The paper addresses potential data contamination and proposes alternative benchmarks to evaluate the model. It also discusses the constraints and limitations of the model, such as being less robust in handling natural language and struggling with tasks involving counting and spatial reasoning. Additionally, it demonstrates the effectiveness of finetuning using synthetic exercises in substantially improving the model's overall performance.
The research paper investigates the impact of high-quality data on training large language models (LLMs) for writing Python functions. It challenges existing scaling laws and demonstrates the effectiveness of using "textbook quality" synthetic and filtered data, combined with finetuning on "textbook-exercise-like" data. The phi-1 model, with 1.3B parameters trained using this approach, achieves significant accuracy on HumanEval and MBPP benchmarks, surpassing larger models. The paper also delves into emergent properties of the model, compares it with a smaller model, proposes alternative benchmarks, and addresses potential data contamination.
Additionally, the authors release the model for community evaluation while maintaining secrecy about certain details of synthetic data generation for proprietary reasons. The paper sheds light on the successful training of LLMs for writing Python functions, presenting a novel approach that challenges existing methods and achieves notable accuracy.
Reference: https://arxiv.org/abs/2306.11644